import logging
import random
import numpy as np

from util.util_log import setup_logging
from util.util_csv import save_csv
from util.util_image import save_img, save_img_xyz, save_line_chart
from util.util_ris_pattern_2 import point_2_phi_pattern, phase_2_pattern, phase_2_pattern_xyz_fft, \
    phase_2_pattern_xyz, eps, phaseBit_2_deg, phase_2_bit

from multi_beam.multi_beam_QE import qe_beam_N

from util.util_analysis_plane import get_peaks, get_peak_nth


# 配置日志，默认打印到控制台，也可以设置打印到文件
# setup_logging()
setup_logging(log_file="../files/dissertation/chapter_4/32x32-d9.3mm-FD0.905/4-(20,45,20,135,20,225,20,315)/log_multi_beam_QE.log")
# 获取日志记录器并记录日志
logger = logging.getLogger("[RIS-multi-beam-QE-1bit]")


# ============================================= 遗传算法 ===========================================
class RISGeneticAlgorithmQE():
    __bit_num = 0       # 比特数
    __beam_num = 0      # 波束数

    def __init__(self, bit_num, beam_num, population_size=50, num_generations=100, num_parents=10, mutation_rate=0.1):
        # 初始化阵列相关
        self.__bit_num = bit_num
        self.__beam_num = beam_num
        # 初始化遗传算法相关
        self.population_size = population_size
        self.num_generations = num_generations
        self.num_parents = num_parents
        self.mutation_rate = mutation_rate
        self.population = None
        self.best_individual = None
        self.best_fitness = None
        self.best_fitness_history = []  # 保存每一代的最佳适应度值
        self.best_individual_history = []  # 保存每一代的最佳适应度值的个体

    def __get_psll(self, pattern):
        psll = 0
        # pattern 转dB
        pattern_dbw = 20 * np.log10(np.abs(pattern) / np.max(np.max(np.abs(pattern))) + eps)
        # pattern找峰值
        peaks = get_peaks(pattern_dbw)
        # 找第beam_num+1峰(小于3dB)作为 PSLL
        if len(peaks) > self.__beam_num:
            peak_nth = get_peak_nth(peaks, self.__beam_num)
            if peak_nth is not None:
                psll = peak_nth[0]
        return psll

    def fitness(self, phaseBit):
        # 相位 X bit转换degree
        phaseDeg = phaseBit_2_deg(phaseBit, self.__bit_num)
        phaseRad = np.deg2rad(phaseDeg)
        # 计算phase_mix的方向图
        # patternBit_mix = phase_2_pattern(phaseBit_mix)                # 公式法直接计算, 准确但速度太慢
        pattern, x, y = phase_2_pattern_xyz_fft(phaseRad)    # FFT法计算, 快速
        # 计算码阵的最大副瓣
        psll = self.__get_psll(pattern)
        return psll

    # def fitness(self, cluster_init):
    #     list_sum = []
    #     for datas in cluster_init:
    #         sum = 0
    #         for data in datas:
    #             sum += data
    #         list_sum.append(sum)
    #     fit = 0
    #     for sum in list_sum:
    #         fit += sum ** 2
    #     return -fit

    def initialize_population(self, phase_mix_init):
        """初始化种群"""
        self.population = [phase_mix_init] * self.population_size

    def selection(self):
        """选择操作"""
        fitness_scores = [self.fitness(individual) for individual in self.population]
        sorted_indices = np.argsort(fitness_scores)  # 从低到高排序
        selected_parents = [self.population[i] for i in sorted_indices[:self.num_parents]]
        # 获取选中的父代个体对应的适应度值
        selected_fitness_scores = [fitness_scores[i] for i in sorted_indices[:self.num_parents]]
        return selected_parents, selected_fitness_scores

    def crossover(self, parents, offspring_size):
        """交叉操作"""
        offspring = []
        for _ in range(offspring_size):
            parent1, parent2 = random.sample(parents, 2)
            parent1 = np.array(parent1)
            parent2 = np.array(parent2)
            # 获取数组的形状
            shape = parent1.shape
            # 将 parent1 和 parent2 扁平化为一维数组
            flat_parent1 = parent1.flatten()
            flat_parent2 = parent2.flatten()
            # 随机选择一个切割位置
            cut_point = random.randint(0, len(flat_parent1))
            # 生成子代
            child = np.concatenate((flat_parent1[:cut_point], flat_parent2[cut_point:]))
            # 将子代重新塑形为原始的二维数组形状
            child = child.reshape(shape)
            child = child.tolist()
            #
            offspring.append(np.array(child))
        return offspring

    def mutation(self, offspring):
        """变异操作"""
        for individual in offspring:
            if random.random() < self.mutation_rate:
                # 获取数组的数量和长度
                num_clusters, array_length = individual.shape
                # 生成一个与 individual 形状相同的随机数数组
                random_values = np.random.uniform(-180, 180, (num_clusters, array_length))
                # 角度转bit
                random_values_bit, random_values_deg = phase_2_bit(random_values, self.__bit_num)
                # 决定哪些元素需要变异
                mask = np.random.rand(num_clusters, array_length) < self.mutation_rate
                # 应用变异
                individual[mask] = random_values_bit[mask]
        return offspring

    def run(self, phaseBit_mix_init):
        logger.info("population_size=%d, num_generations=%d, num_parents=%d, mutation_rate=%d"
                    % (self.population_size, self.num_generations, self.num_parents, self.mutation_rate))
        # """初始化返回值"""
        self.best_fitness = self.fitness(phaseBit_mix_init)
        self.best_individual = phaseBit_mix_init
        self.best_fitness_history = []
        self.best_individual_history = []
        # """运行遗传算法 -- 初始化阶段"""
        self.initialize_population(phaseBit_mix_init)
        # """运行遗传算法 -- 搜索阶段"""
        for generation in range(self.num_generations):
            # 选择操作
            selected_parents, selected_fitness_scores = self.selection()
            # 交换操作
            offspring = self.crossover(selected_parents, self.population_size - self.num_parents)
            # 变异操作
            offspring = self.mutation(offspring)
            # 计算后代的适应度值
            offspring_fitness_scores = [self.fitness(individual) for individual in offspring]
            # 更新种群
            self.population = selected_parents + offspring
            # 合并父代和后代的适应度值
            all_fitness_scores = selected_fitness_scores + offspring_fitness_scores
            #
            # 找到当前代的最佳个体
            this_best_index = np.argmax(all_fitness_scores)  # 找到最佳适应度值的索引
            if this_best_index < len(selected_parents):
                this_best_individual = selected_parents[this_best_index]  # 最佳个体来自父代
            else:
                this_best_individual = offspring[this_best_index - len(selected_parents)]  # 最佳个体来自后代
            this_best_fitness = all_fitness_scores[this_best_index]  # 最佳适应度值
            #
            # 更新最佳适应度
            if this_best_fitness < self.best_fitness:
                self.best_fitness = this_best_fitness
                self.best_individual = this_best_individual
            # 记录最佳适应度曲线
            self.best_fitness_history.append(self.best_fitness)
            self.best_individual_history.append(self.best_individual)
            #
            # logger.info("generation=%d: self.best_fitness=%f, self.best_individual:%s"
            #             % (generation, self.best_fitness, self.best_individual))
            logger.info("generation=%d: self.best_fitness=%f" % (generation, self.best_fitness))
        return self.best_individual, self.best_fitness, self.best_fitness_history, self.best_individual_history


# ============================================= 主函数 ====================================
# GA-QE 核心算法
def ga_qe_beam_N(phaseBit_list, beam_num, bit_num):
    # 1.QE方式初始化结果
    phaseBit_mix, random_indices = qe_beam_N(phaseBit_list)
    # 2.遗传算法(GA)寻找最优的phaseBit_mix
    ga = RISGeneticAlgorithmQE(bit_num, beam_num, 50, 30, 10, 0.2)
    best_individual, best_fitness, best_fitness_history, best_individual_history = ga.run(phaseBit_mix)
    # logger.info("best_individual:%s" % (best_individual))
    logger.info("Best Fitness=%f" % (best_fitness))
    #
    return best_individual, best_fitness, best_fitness_history, best_individual_history


# 主方法: 量化选举 + GA  -- N波束
def main_multi_beam_N(points, path_pre, bit_num):
    logger.info("QE.main_multi_beam_N: bit_num=%d, path_pre=%s, " % (bit_num, path_pre))
    logger.info("QE.main_multi_beam_N: num of points = %d" % (len(points)))
    logger.info("QE.main_multi_beam_N: points = %s" % (points))
    # 目前只支持2bit
    if bit_num > 2:
        logger.error("main_multi_beam_N: bit_num bigger than 2.")
        return
    phase_pattern_list = []
    phaseBit_list = []
    for point in points:
        theta = point[0]
        phi = point[1]
        phase, phaseBit, pattern = point_2_phi_pattern(theta, phi, bit_num)
        phase_pattern_list.append([phase, phaseBit, pattern])
        phaseBit_list.append(phaseBit)
    # QE + GA
    phaseBit_mix, best_fitness, best_fitness_history, best_individual_history \
        = ga_qe_beam_N(phaseBit_list, len(points), bit_num)
    phaseBitDeg_mix = phaseBit_2_deg(phaseBit_mix, bit_num)
    # 计算phase_mix的方向图
    phaseBit_mix = np.deg2rad(phaseBitDeg_mix)
    patternBit_mix = phase_2_pattern(phaseBit_mix)
    # 保存结果
    logger.info("save QE multi-beam N result...")
    patternBit_mix_xyz, x, y, z = phase_2_pattern_xyz(phaseBit_mix)
    # 保存结果
    for i in range(len(phase_pattern_list)):
        phase = phase_pattern_list[i][0]
        phaseBit = phase_pattern_list[i][1]
        pattern = phase_pattern_list[i][2]
        # 保存图片
        save_img(path_pre + "phase" + str(i+1) + ".jpg", phase)
        save_img(path_pre + "phaseBit" + str(i+1) + ".jpg", phaseBit)
        save_img(path_pre + "pattern" + str(i+1) + ".jpg", pattern)
        # 保存相位结果
        save_csv(phase, path_pre + "phase" + str(i+1) + ".csv")
        save_csv(phaseBit, path_pre + "phaseBit" + str(i+1) + ".csv")
    # 保存图片
    save_img(path_pre + "phaseBit_mix.jpg", phaseBit_mix)       # 量化选举法 -- 结果码阵
    save_img(path_pre + "patternBit_mix.jpg", patternBit_mix)   # 量化选举法 -- 结果码阵方向图
    save_img_xyz(path_pre + "patternBit_mix_xyz.jpg", np.abs(patternBit_mix_xyz), x, y)
    # 保存相位结果
    save_csv(phaseBit_mix, path_pre + "phaseBit_mix.csv")
    # 保存遗传算法优化结果
    save_line_chart(path_pre + "best_fitness_history.jpg", best_fitness_history,
                    "best_fitness_history", "iteration", "best_fitness", "fitness")
    save_csv([[item] for item in best_fitness_history], path_pre + "best_fitness_history.csv")
    save_csv(best_individual_history, path_pre + "best_individual_history.csv")


# ======================================================= main 主方法 ===============================================
def main_multi_ga_qe():
    # 基于GA-QE的方法: 主函数
    # 测试方法
    #
    # GA-QE: 主函数
    # main_multi_beam_N([[30, 0], [30, 180]],
    #                   f"E://project/ant_sim/files/multi-beam/1bit/GA-QE/2-(30,0,30,180)/", 1)
    # main_multi_beam_N([[30, 0], [30, 90]],
    #                   f"E://project/ant_sim/files/multi-beam/1bit/GA-QE/2-(30,0,30,90)/", 1)
    # main_multi_beam_N([[30, 0], [30, 60], [30, 120], [30, 180]],
    #                   f"E://project/ant_sim/files/multi-beam/1bit/GA-QE/4-(30,0,30,60,30,120,30,180)/", 1)
    main_multi_beam_N([[20, 45], [20, 135], [20, 225], [20, 315]],
                      f"../files/dissertation/chapter_4/32x32-d9.3mm-FD0.905/4-(20,45,20,135,20,225,20,315)/", 1)
    # main_multi_beam_N([[30, 0], [30, 45], [30, 90], [30, 135], [30, 180], [30, 225], [30, 270], [30, 315]],
    #                   f"../files/dissertation/chapter_4/32x32-d9.3mm-FD0.905/8-(30,45step)/", 1)
    # main_multi_beam_N([[30, 0], [30, 22.5], [30, 45], [30, 67.5], [30, 90], [30, 112.5], [30, 135], [30, 157.5],
    #                    [30, 180], [30, 202.5], [30, 225], [30, 247.5], [30, 270], [30, 292.5], [30, 315], [30, 337.5]],
    #                   f"E://project/ant_sim/files/multi-beam/1bit/GA-QE/16-(30,22.5step)/", 1)
    #
    # main_multi_beam_N([[30, 0], [30, 180]],
    #                   f"E://project/ant_sim/files/multi-beam/2bit/GA-QE/2-(30,0,30,180)/", 2)
    # main_multi_beam_N([[30, 0], [30, 90]],
    #                   f"E://project/ant_sim/files/multi-beam/2bit/GA-QE/2-(30,0,30,90)/", 2)
    # main_multi_beam_N([[30, 0], [30, 60], [30, 120], [30, 180]],
    #                   f"E://project/ant_sim/files/multi-beam/2bit/GA-QE/4-(30,0,30,60,30,120,30,180)/", 2)
    # main_multi_beam_N([[30, 0], [30, 90], [30, 180], [30, 270]],
    #                   f"E://project/ant_sim/files/multi-beam/2bit/GA-QE/4-(30,0,30,90,30,180,30,270)/", 2)
    # main_multi_beam_N([[30, 0], [30, 45], [30, 90], [30, 135], [30, 180], [30, 225], [30, 270], [30, 315]],
    #                   f"E://project/ant_sim/files/multi-beam/2bit/GA-QE/8-(30,45step)/", 2)
    # main_multi_beam_N([[30, 0], [30, 22.5], [30, 45], [30, 67.5], [30, 90], [30, 112.5], [30, 135], [30, 157.5],
    #                    [30, 180], [30, 202.5], [30, 225], [30, 247.5], [30, 270], [30, 292.5], [30, 315], [30, 337.5]],
    #                   f"E://project/ant_sim/files/multi-beam/2bit/GA-QE/16-(30,22.5step)/", 2)





if __name__ == '__main__':
    logger.info("1bit-RIS-multi-beam-QE: GA based on QE method")
    main_multi_ga_qe()